mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications

By Oresti Banos, Rafael García, J. A. H. Terriza, Miguel Damas, Héctor Pomares, Ignacio Rojas, Alejandro Saez, Claudia Villalonga. International Workshop on Ambient Assisted Living and Home Care, 2014

507 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, General Health Professions

View Paper: https://www.semanticscholar.org/paper/569518638ad7ec69003a990dfc929efa91006e11

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors (2020, methodology): “We show that the proposed architecture gives comparable performance for different sample generation schemes on the MHEALTH, USC-HAD, UTD-MHAD, WHARF and WISDM datasets.”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “6 50–200 Hz 37 Daily & Sports Activities [19] Locomotion 9D IMU 19 25 Hz 37 MHEALTH [30, 31] Locomotion & gesture 9D IMU & ECG 12 50 Hz 33 Opportunity [59] Locomotion & gesture 9D IMU 16 50 Hz 32 PAMAP2 [228] Locomotion & activities 9D IMU & HR monitor 18 100 Hz 32 Daphnet [23] Freezing of gait 3D A”
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016, methodology): “A number of datasets have been published for activity recognition, including the OPPORTUNITY [36], PAMAP [37], Skoda [38] and mHealth [39] datasets.”
Advanced internet of things for personalised healthcare systems: A survey (2017, background): “…the phone in different part of one’s body as the results may diverse from phone putting from tops to trousers. mHealthDroid (Mobile Health Android) [138] is an open source framework designed to facilitate the rapid and easy development of biomedical android application which is available on Google…”
Recent trends in machine learning for human activity recognition—A survey (2018, background): “, 2013), Daily And Sports (Barshan & Yüksek, 2013), MHealth (Banos et al., 2014) datasets and have proved that a HAR model can be trained using a reduced number of instances.”
A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020, background): “After eliminating the impact of “others”, the overall classiﬁcation performance on MHEALTH and PAMAP2 is boosted.”